# Multi-task learning with compressible features for Collaborative Intelligence

arXiv: https://arxiv.org/abs/1902.05179 · HTML: https://mlsanity.com/paper/1902.05179/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2019.

## Built on by (most cited first, 20 of 20)

- [Collaborative Intelligence: Challenges and Opportunities](https://mlsanity.com/paper/2102.06841/) (2021)
- [Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics](https://mlsanity.com/paper/2110.09241/) (2021)
- [DMOFC: Discrimination Metric-Optimized Feature Compression](https://mlsanity.com/paper/2405.04044/) (2024)
- [Rate-Distortion in Image Coding for Machines](https://mlsanity.com/paper/2209.11694/) (2022)
- [Machine Perception-Driven Image Compression: A Layered Generative Approach](https://mlsanity.com/paper/2304.06896/) (2023)
- [DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation](https://mlsanity.com/paper/2506.16495/) (2025)
- [Deep Joint Source-Channel Coding for Multi-Task Network](https://mlsanity.com/paper/2109.05779/) (2021)
- [A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing](https://mlsanity.com/paper/2208.11596/) (2022)
- [A New Image Codec Paradigm for Human and Machine Uses](https://mlsanity.com/paper/2112.10071/) (2021)
- [Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines](https://mlsanity.com/paper/2503.19278/) (2025)
- [Compressed Feature Quality Assessment: Dataset and Baselines](https://mlsanity.com/paper/2506.07412/) (2025)
- [SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences](https://mlsanity.com/paper/2112.14934/) (2021)
- [Privacy-Preserving Feature Coding for Machines](https://mlsanity.com/paper/2210.00727/) (2022)
- [Cross-architecture universal feature coding via distribution alignment](https://mlsanity.com/paper/2506.12737/) (2025)
- [License Plate Privacy in Collaborative Visual Analysis of Traffic Scenes](https://mlsanity.com/paper/2205.01724/) (2022)
- [Policy Search, Retrieval, and Composition via Task Similarity in Collaborative Agentic Systems](https://mlsanity.com/paper/2506.05577/) (2025)
- [Latent-Space Inpainting for Packet Loss Concealment in Collaborative Object Detection](https://mlsanity.com/paper/2102.00142/) (2021)
- [Analysis of Latent-Space Motion for Collaborative Intelligence](https://mlsanity.com/paper/2102.04018/) (2021)
- [DFTS2: Simulating Deep Feature Transmission Over Packet Loss Channels](https://mlsanity.com/paper/2112.00794/) (2021)
- [Trainable Bitwise Soft Quantization for Input Feature Compression](https://mlsanity.com/paper/2603.05172/) (2026)

## Abstract (arXiv)

A promising way to deploy Artificial Intelligence (AI)-based services on mobile devices is to run a part of the AI model (a deep neural network) on the mobile itself, and the rest in the cloud. This is sometimes referred to as collaborative intelligence. In this framework, intermediate features from the deep network need to be transmitted to the cloud for further processing. We study the case where such features are used for multiple purposes in the cloud (multi-tasking) and where they need to be compressible in order to allow efficient transmission to the cloud. To this end, we introduce a new loss function that encourages feature compressibility while improving system performance on multiple tasks. Experimental results show that with the compression-friendly loss, one can achieve around 20% bitrate reduction without sacrificing the performance on several vision-related tasks.